Table REF  contains the state-of-the-art comparison of methods that use word-level LMs for line-level decoding. The majority of the methods reported utilize several techniques at the same time in order to further improve the performance of their system, including hybrid LMs [1]}, [2]}, [3]}, Lexicon [4]}, [5]}, e.t.c.
Furthermore, methods [1]}, [2]}, [3]} perform paragraph-level decoding in order to maximize the possible context and consequently enhance the impact of word-level LMs.
Contrary to high complexity architectures (e.g. 2d BiLSTMs) and high complexity decoding schemes, as the aforementioned, the proposed approach achieves recognition results very close to SoA, with minor inference and decoding overhead.
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